R [1]} is often cited by Bayesian statisticians as their favorite programming language due to the many packages that provide tools for Bayesian inference.
The general program for Bayesian inference BUGS [2]} has been available for a couple of decades, with interfaces in R.
Since then, additional software has been developed to make that language more accessible to the users, for instance OpenBUGS [3]}, JAGS [4]}, and Stan [5]}.
All three can be accessed directly from R by respectively using R2OpenBUGS/R2WinBUGS [6]}, rjags [7]}, runjags [8]}, and rstan [9]}.
Programs for specific fields of Bayesian statistics have appeared in recent years, for instance
bspmma [10]} for meta-analysis using DPM models,
DPpackage [11]}, [12]}, a bundle of functions for Bayesian nonparametric models,
BNPmix [13]}, a set of functions for density estimation with Dirichlet process and Pitman–Yor mixing measures via marginal algorithms,
PReMiuM [14]} for profile regression using the Dirichlet process,
Biips [15]} for Bayesian inference via particle filtering,
Bayesian Regression [16]} for Bayesian nonparametric regression.
Packages mcclust [17]}, mcclust.ext [18]} and GreedyEPL [19]} provide point estimation and credible sets for Bayesian cluster analysis.
The interested reader may refer to the CRAN Task View on Bayesian Inference
for an extensive list of R packages dedicated to Bayesian statistics (see Section  for a more detailed discussion of R packages for Bayesian density estimation).
